We introduce a method to synthesize animator guided human motion across 3D
scenes. Given a set of sparse (3 or 4) joint locations (such as the location of
a person's hand and two feet) and a seed motion sequence in a 3D scene, our
method generates a plausible motion sequence starting from the seed motion
while satisfying the constraints imposed by the provided keypoints. We
decompose the continual motion synthesis problem into walking along paths and
transitioning in and out of the actions specified by the keypoints, which
enables long generation of motions that satisfy scene constraints without
explicitly incorporating scene information. Our method is trained only using
scene agnostic mocap data. As a result, our approach is deployable across 3D
scenes with various geometries. For achieving plausible continual motion
synthesis without drift, our key contribution is to generate motion in a
goal-centric canonical coordinate frame where the next immediate target is
situated at the origin. Our model can generate long sequences of diverse
actions such as grabbing, sitting and leaning chained together in arbitrary
order, demonstrated on scenes of varying geometry: HPS, Replica, Matterport,
ScanNet and scenes represented using NeRFs. Several experiments demonstrate
that our method outperforms existing methods that navigate paths in 3D scenes.

我们介绍了一种利用稀疏关键点在三维场景中合成动画指导人类运动的方法，该方法通过将连续运动合成问题分解为沿路径行走和在关键点指定的动作之间的转换，生成长序列的动作，并在目标为原点的规范坐标系中生成运动以实现持续运动合成。